Overview

Dataset statistics

Number of variables25
Number of observations24849
Missing cells9079
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 MiB
Average record size in memory707.4 B

Variable types

Text2
Categorical5
Numeric15
DateTime2
Boolean1

Alerts

country_code has constant value "GB"Constant
currency_native has constant value "USD"Constant
scraped_during_month has constant value "True"Constant
listing_type is highly imbalanced (56.3%)Imbalance
bedrooms is highly imbalanced (50.2%)Imbalance
city is highly imbalanced (57.5%)Imbalance
cleaning_fee has 9013 (36.3%) missing valuesMissing
cleaning_fee has 4494 (18.1%) zerosZeros
blocked_days has 5097 (20.5%) zerosZeros
occupancy_rate has 515 (2.1%) zerosZeros
reservation_days has 515 (2.1%) zerosZeros
number_of_reservation has 4837 (19.5%) zerosZeros
revenue_usd has 475 (1.9%) zerosZeros
revenue_native has 475 (1.9%) zerosZeros

Reproduction

Analysis started2024-05-17 20:46:19.945365
Analysis finished2024-05-17 20:47:09.638579
Duration49.69 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-05-17T22:47:09.772351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length34
Median length32
Mean length17.041974
Min length3

Characters and Unicode

Total characters423476
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEntire home
2nd rowEntire rental unit
3rd rowPrivate room in rental unit
4th rowEntire townhouse
5th rowEntire rental unit
ValueCountFrequency (%)
entire 18402
25.6%
unit 8408
11.7%
rental 8408
11.7%
home 6267
 
8.7%
room 6257
 
8.7%
in 6242
 
8.7%
private 5134
 
7.1%
condo 4436
 
6.2%
townhouse 2062
 
2.9%
hotel 957
 
1.3%
Other values (34) 5358
 
7.4%
2024-05-17T22:47:10.451095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 49356
11.7%
t 47915
11.3%
47082
11.1%
e 46662
11.0%
i 40421
9.5%
r 38764
9.2%
o 34960
8.3%
E 18402
 
4.3%
a 16768
 
4.0%
u 14055
 
3.3%
Other values (25) 69091
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 49356
11.7%
t 47915
11.3%
47082
11.1%
e 46662
11.0%
i 40421
9.5%
r 38764
9.2%
o 34960
8.3%
E 18402
 
4.3%
a 16768
 
4.0%
u 14055
 
3.3%
Other values (25) 69091
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 49356
11.7%
t 47915
11.3%
47082
11.1%
e 46662
11.0%
i 40421
9.5%
r 38764
9.2%
o 34960
8.3%
E 18402
 
4.3%
a 16768
 
4.0%
u 14055
 
3.3%
Other values (25) 69091
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 49356
11.7%
t 47915
11.3%
47082
11.1%
e 46662
11.0%
i 40421
9.5%
r 38764
9.2%
o 34960
8.3%
E 18402
 
4.3%
a 16768
 
4.0%
u 14055
 
3.3%
Other values (25) 69091
16.3%

listing_type
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
entire_home
18592 
private_room
6057 
hotel_room
 
122
shared_room
 
78

Length

Max length12
Median length11
Mean length11.238843
Min length10

Characters and Unicode

Total characters279274
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowentire_home
2nd rowentire_home
3rd rowprivate_room
4th rowentire_home
5th rowentire_home

Common Values

ValueCountFrequency (%)
entire_home 18592
74.8%
private_room 6057
 
24.4%
hotel_room 122
 
0.5%
shared_room 78
 
0.3%

Length

2024-05-17T22:47:10.651926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T22:47:10.815122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
entire_home 18592
74.8%
private_room 6057
 
24.4%
hotel_room 122
 
0.5%
shared_room 78
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 62033
22.2%
o 31228
11.2%
r 30984
11.1%
_ 24849
8.9%
m 24849
8.9%
t 24771
 
8.9%
i 24649
 
8.8%
h 18792
 
6.7%
n 18592
 
6.7%
a 6135
 
2.2%
Other values (5) 12392
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 62033
22.2%
o 31228
11.2%
r 30984
11.1%
_ 24849
8.9%
m 24849
8.9%
t 24771
 
8.9%
i 24649
 
8.8%
h 18792
 
6.7%
n 18592
 
6.7%
a 6135
 
2.2%
Other values (5) 12392
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 62033
22.2%
o 31228
11.2%
r 30984
11.1%
_ 24849
8.9%
m 24849
8.9%
t 24771
 
8.9%
i 24649
 
8.8%
h 18792
 
6.7%
n 18592
 
6.7%
a 6135
 
2.2%
Other values (5) 12392
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 62033
22.2%
o 31228
11.2%
r 30984
11.1%
_ 24849
8.9%
m 24849
8.9%
t 24771
 
8.9%
i 24649
 
8.8%
h 18792
 
6.7%
n 18592
 
6.7%
a 6135
 
2.2%
Other values (5) 12392
 
4.4%

bedrooms
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
13680 
2
5707 
3
2464 
4
1504 
Studio
 
688
Other values (9)
 
806

Length

Max length6
Median length1
Mean length1.1395227
Min length1

Characters and Unicode

Total characters28316
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row6
5th row1

Common Values

ValueCountFrequency (%)
1 13680
55.1%
2 5707
23.0%
3 2464
 
9.9%
4 1504
 
6.1%
Studio 688
 
2.8%
5 526
 
2.1%
6 136
 
0.5%
0 57
 
0.2%
7 45
 
0.2%
11 12
 
< 0.1%
Other values (4) 30
 
0.1%

Length

2024-05-17T22:47:10.980546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 13680
55.1%
2 5707
23.0%
3 2464
 
9.9%
4 1504
 
6.1%
studio 688
 
2.8%
5 526
 
2.1%
6 136
 
0.5%
0 57
 
0.2%
7 45
 
0.2%
11 12
 
< 0.1%
Other values (4) 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 13719
48.4%
2 5719
20.2%
3 2464
 
8.7%
4 1504
 
5.3%
S 688
 
2.4%
t 688
 
2.4%
u 688
 
2.4%
d 688
 
2.4%
i 688
 
2.4%
o 688
 
2.4%
Other values (6) 782
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 13719
48.4%
2 5719
20.2%
3 2464
 
8.7%
4 1504
 
5.3%
S 688
 
2.4%
t 688
 
2.4%
u 688
 
2.4%
d 688
 
2.4%
i 688
 
2.4%
o 688
 
2.4%
Other values (6) 782
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 13719
48.4%
2 5719
20.2%
3 2464
 
8.7%
4 1504
 
5.3%
S 688
 
2.4%
t 688
 
2.4%
u 688
 
2.4%
d 688
 
2.4%
i 688
 
2.4%
o 688
 
2.4%
Other values (6) 782
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 13719
48.4%
2 5719
20.2%
3 2464
 
8.7%
4 1504
 
5.3%
S 688
 
2.4%
t 688
 
2.4%
u 688
 
2.4%
d 688
 
2.4%
i 688
 
2.4%
o 688
 
2.4%
Other values (6) 782
 
2.8%

bathrooms
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4504004
Minimum0
Maximum9
Zeros93
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:11.267406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7837984
Coefficient of variation (CV)0.54040139
Kurtosis9.261414
Mean1.4504004
Median Absolute Deviation (MAD)0
Skewness2.360229
Sum36041
Variance0.61433993
MonotonicityNot monotonic
2024-05-17T22:47:11.411020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 16558
66.6%
2 6023
 
24.2%
3 1517
 
6.1%
4 519
 
2.1%
0 93
 
0.4%
5 84
 
0.3%
6 23
 
0.1%
7 12
 
< 0.1%
8 12
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
0 93
 
0.4%
1 16558
66.6%
2 6023
 
24.2%
3 1517
 
6.1%
4 519
 
2.1%
5 84
 
0.3%
6 23
 
0.1%
7 12
 
< 0.1%
8 12
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
9 8
 
< 0.1%
8 12
 
< 0.1%
7 12
 
< 0.1%
6 23
 
0.1%
5 84
 
0.3%
4 519
 
2.1%
3 1517
 
6.1%
2 6023
 
24.2%
1 16558
66.6%
0 93
 
0.4%

country_code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
GB
24849 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49698
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGB
2nd rowGB
3rd rowGB
4th rowGB
5th rowGB

Common Values

ValueCountFrequency (%)
GB 24849
100.0%

Length

2024-05-17T22:47:11.556527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T22:47:11.667997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gb 24849
100.0%

Most occurring characters

ValueCountFrequency (%)
G 24849
50.0%
B 24849
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 24849
50.0%
B 24849
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 24849
50.0%
B 24849
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 24849
50.0%
B 24849
50.0%

city
Categorical

IMBALANCE 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Brighton and Hove
12588 
Brighton
4768 
The City of Brighton and Hove
2924 
Kemptown
1772 
Hove
1373 
Other values (34)
1424 

Length

Max length29
Median length17
Mean length15.045193
Min length2

Characters and Unicode

Total characters373858
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrighton
2nd rowBrighton
3rd rowBrighton
4th rowBrighton
5th rowHove

Common Values

ValueCountFrequency (%)
Brighton and Hove 12588
50.7%
Brighton 4768
 
19.2%
The City of Brighton and Hove 2924
 
11.8%
Kemptown 1772
 
7.1%
Hove 1373
 
5.5%
Brighton Marina 417
 
1.7%
Portslade 274
 
1.1%
Brighton & Hove 257
 
1.0%
Brighton 121
 
0.5%
Southwick 51
 
0.2%
Other values (29) 304
 
1.2%

Length

2024-05-17T22:47:11.816254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brighton 21138
32.2%
hove 17191
26.2%
and 15528
23.6%
the 2924
 
4.4%
city 2924
 
4.4%
of 2924
 
4.4%
kemptown 1782
 
2.7%
marina 417
 
0.6%
portslade 274
 
0.4%
257
 
0.4%
Other values (24) 379
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 43403
11.6%
41050
11.0%
n 39025
10.4%
t 26313
 
7.0%
i 24619
 
6.6%
h 24153
 
6.5%
e 22420
 
6.0%
r 21909
 
5.9%
g 21169
 
5.7%
B 21154
 
5.7%
Other values (32) 88643
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 373858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 43403
11.6%
41050
11.0%
n 39025
10.4%
t 26313
 
7.0%
i 24619
 
6.6%
h 24153
 
6.5%
e 22420
 
6.0%
r 21909
 
5.9%
g 21169
 
5.7%
B 21154
 
5.7%
Other values (32) 88643
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 373858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 43403
11.6%
41050
11.0%
n 39025
10.4%
t 26313
 
7.0%
i 24619
 
6.6%
h 24153
 
6.5%
e 22420
 
6.0%
r 21909
 
5.9%
g 21169
 
5.7%
B 21154
 
5.7%
Other values (32) 88643
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 373858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 43403
11.6%
41050
11.0%
n 39025
10.4%
t 26313
 
7.0%
i 24619
 
6.6%
h 24153
 
6.5%
e 22420
 
6.0%
r 21909
 
5.9%
g 21169
 
5.7%
B 21154
 
5.7%
Other values (32) 88643
23.7%
Distinct1413
Distinct (%)5.7%
Missing66
Missing (%)0.3%
Memory size1.5 MiB
2024-05-17T22:47:12.263011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0150103
Min length7

Characters and Unicode

Total characters173853
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)0.3%

Sample

1st rowBN1 1BG
2nd rowBN1 4AQ
3rd rowBN3 4GP
4th rowBN2 9XH
5th rowBN3 3UA
ValueCountFrequency (%)
bn1 10045
20.3%
bn2 8158
 
16.5%
bn3 6208
 
12.5%
bn41 303
 
0.6%
1et 282
 
0.6%
1ae 279
 
0.6%
2fg 220
 
0.4%
2ps 191
 
0.4%
5xz 169
 
0.3%
1pg 156
 
0.3%
Other values (1172) 23555
47.5%
2024-05-17T22:47:12.889768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 27822
16.0%
N 27263
15.7%
24783
14.3%
1 17589
10.1%
2 11659
 
6.7%
3 10356
 
6.0%
E 4313
 
2.5%
A 4149
 
2.4%
P 3320
 
1.9%
D 3245
 
1.9%
Other values (21) 39354
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 27822
16.0%
N 27263
15.7%
24783
14.3%
1 17589
10.1%
2 11659
 
6.7%
3 10356
 
6.0%
E 4313
 
2.5%
A 4149
 
2.4%
P 3320
 
1.9%
D 3245
 
1.9%
Other values (21) 39354
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 27822
16.0%
N 27263
15.7%
24783
14.3%
1 17589
10.1%
2 11659
 
6.7%
3 10356
 
6.0%
E 4313
 
2.5%
A 4149
 
2.4%
P 3320
 
1.9%
D 3245
 
1.9%
Other values (21) 39354
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 27822
16.0%
N 27263
15.7%
24783
14.3%
1 17589
10.1%
2 11659
 
6.7%
3 10356
 
6.0%
E 4313
 
2.5%
A 4149
 
2.4%
P 3320
 
1.9%
D 3245
 
1.9%
Other values (21) 39354
22.6%

latitude
Real number (ℝ)

Distinct2047
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.828409
Minimum50.807684
Maximum50.87611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:13.240447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50.807684
5-th percentile50.81794
Q150.82207
median50.82646
Q350.832315
95-th percentile50.847
Maximum50.87611
Range0.068426246
Interquartile range (IQR)0.010245355

Descriptive statistics

Standard deviation0.0094668918
Coefficient of variation (CV)0.00018625198
Kurtosis2.7523398
Mean50.828409
Median Absolute Deviation (MAD)0.00503
Skewness1.3969375
Sum1263035.1
Variance8.962204 × 10-5
MonotonicityNot monotonic
2024-05-17T22:47:13.613216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.823 540
 
2.2%
50.824 442
 
1.8%
50.826 438
 
1.8%
50.82 422
 
1.7%
50.825 406
 
1.6%
50.827 299
 
1.2%
50.819 280
 
1.1%
50.829 270
 
1.1%
50.821 264
 
1.1%
50.828 251
 
1.0%
Other values (2037) 21237
85.5%
ValueCountFrequency (%)
50.80768375 3
 
< 0.1%
50.80877 12
< 0.1%
50.809 2
 
< 0.1%
50.80943 12
< 0.1%
50.81 16
0.1%
50.81005 3
 
< 0.1%
50.81013 3
 
< 0.1%
50.81025 7
< 0.1%
50.81029 1
 
< 0.1%
50.81031 12
< 0.1%
ValueCountFrequency (%)
50.87611 1
 
< 0.1%
50.87401 2
 
< 0.1%
50.87055 11
< 0.1%
50.87025 11
< 0.1%
50.87009 12
< 0.1%
50.86913 11
< 0.1%
50.869 2
 
< 0.1%
50.86874 4
 
< 0.1%
50.86862 4
 
< 0.1%
50.86786 11
< 0.1%

longitude
Real number (ℝ)

Distinct2678
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.14507685
Minimum-0.22709
Maximum-0.04482
Zeros0
Zeros (%)0.0%
Negative24849
Negative (%)100.0%
Memory size194.3 KiB
2024-05-17T22:47:13.902773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.22709
5-th percentile-0.189
Q1-0.15693
median-0.14208
Q3-0.13101
95-th percentile-0.1062
Maximum-0.04482
Range0.18227
Interquartile range (IQR)0.02592

Descriptive statistics

Standard deviation0.024412851
Coefficient of variation (CV)-0.16827531
Kurtosis1.1893804
Mean-0.14507685
Median Absolute Deviation (MAD)0.01255
Skewness-0.27440994
Sum-3605.0146
Variance0.00059598731
MonotonicityNot monotonic
2024-05-17T22:47:14.097175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.142 182
 
0.7%
-0.139 179
 
0.7%
-0.135 175
 
0.7%
-0.143 174
 
0.7%
-0.15 171
 
0.7%
-0.158 166
 
0.7%
-0.136 163
 
0.7%
-0.156 160
 
0.6%
-0.14 154
 
0.6%
-0.13 148
 
0.6%
Other values (2668) 23177
93.3%
ValueCountFrequency (%)
-0.22709 10
< 0.1%
-0.226 2
 
< 0.1%
-0.225 4
 
< 0.1%
-0.2241231838 2
 
< 0.1%
-0.22347 4
 
< 0.1%
-0.22324 9
< 0.1%
-0.22313 3
 
< 0.1%
-0.22295 8
< 0.1%
-0.22256 12
< 0.1%
-0.22237 3
 
< 0.1%
ValueCountFrequency (%)
-0.04482 11
< 0.1%
-0.05791 5
 
< 0.1%
-0.05954 8
< 0.1%
-0.06 13
0.1%
-0.06062 4
 
< 0.1%
-0.061 6
< 0.1%
-0.062 5
 
< 0.1%
-0.06219 2
 
< 0.1%
-0.06228 3
 
< 0.1%
-0.06304 3
 
< 0.1%

currency_native
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
USD
24849 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74547
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 24849
100.0%

Length

2024-05-17T22:47:14.276323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T22:47:14.393042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
usd 24849
100.0%

Most occurring characters

ValueCountFrequency (%)
U 24849
33.3%
S 24849
33.3%
D 24849
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 24849
33.3%
S 24849
33.3%
D 24849
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 24849
33.3%
S 24849
33.3%
D 24849
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 24849
33.3%
S 24849
33.3%
D 24849
33.3%

airbnb_property_id
Real number (ℝ)

Distinct3928
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2287281 × 1017
Minimum74819
Maximum9.8614365 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:14.540352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum74819
5-th percentile3979489
Q124214574
median46274683
Q36.1421009 × 1017
95-th percentile8.8053443 × 1017
Maximum9.8614365 × 1017
Range9.8614365 × 1017
Interquartile range (IQR)6.1421009 × 1017

Descriptive statistics

Standard deviation3.4522334 × 1017
Coefficient of variation (CV)1.5489702
Kurtosis-0.87355516
Mean2.2287281 × 1017
Median Absolute Deviation (MAD)27349779
Skewness0.98067943
Sum4.1431653 × 1018
Variance1.1917915 × 1035
MonotonicityNot monotonic
2024-05-17T22:47:14.739301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74819 12
 
< 0.1%
36368917 12
 
< 0.1%
36719040 12
 
< 0.1%
36878711 12
 
< 0.1%
37100138 12
 
< 0.1%
37138753 12
 
< 0.1%
37144071 12
 
< 0.1%
37212162 12
 
< 0.1%
37239145 12
 
< 0.1%
37289228 12
 
< 0.1%
Other values (3918) 24729
99.5%
ValueCountFrequency (%)
74819 12
< 0.1%
98654 3
 
< 0.1%
146850 1
 
< 0.1%
230787 6
< 0.1%
280982 12
< 0.1%
282004 12
< 0.1%
301663 1
 
< 0.1%
330733 2
 
< 0.1%
338466 12
< 0.1%
339054 12
< 0.1%
ValueCountFrequency (%)
9.861436506 × 10171
< 0.1%
9.853896624 × 10172
< 0.1%
9.853267247 × 10172
< 0.1%
9.845939358 × 10171
< 0.1%
9.831649149 × 10171
< 0.1%
9.803914576 × 10171
< 0.1%
9.798282097 × 10172
< 0.1%
9.79736385 × 10172
< 0.1%
9.796508649 × 10171
< 0.1%
9.79626223 × 10171
< 0.1%

airbnb_host_id
Real number (ℝ)

Distinct2454
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4621234 × 108
Minimum32786
Maximum5.3821624 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:14.926138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum32786
5-th percentile3354557.4
Q120769306
median77640296
Q32.429231 × 108
95-th percentile4.6387144 × 108
Maximum5.3821624 × 108
Range5.3818346 × 108
Interquartile range (IQR)2.2215379 × 108

Descriptive statistics

Standard deviation1.5542777 × 108
Coefficient of variation (CV)1.0630277
Kurtosis-0.36710968
Mean1.4621234 × 108
Median Absolute Deviation (MAD)68686588
Skewness0.98485445
Sum3.6332305 × 1012
Variance2.4157792 × 1016
MonotonicityNot monotonic
2024-05-17T22:47:15.115777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9802857 645
 
2.6%
183539480 579
 
2.3%
96169037 311
 
1.3%
279714013 283
 
1.1%
190150800 278
 
1.1%
298960871 272
 
1.1%
402695524 230
 
0.9%
394289502 157
 
0.6%
449399092 156
 
0.6%
116513439 133
 
0.5%
Other values (2444) 21805
87.8%
ValueCountFrequency (%)
32786 8
< 0.1%
70728 7
< 0.1%
75775 7
< 0.1%
116489 1
 
< 0.1%
117767 2
 
< 0.1%
127009 3
 
< 0.1%
136985 7
< 0.1%
144058 5
< 0.1%
150889 7
< 0.1%
165249 12
< 0.1%
ValueCountFrequency (%)
538216244 2
< 0.1%
538027701 1
< 0.1%
536976317 2
< 0.1%
536896805 2
< 0.1%
536813453 1
< 0.1%
536789832 2
< 0.1%
535519196 2
< 0.1%
534641757 1
< 0.1%
534491647 2
< 0.1%
531998812 2
< 0.1%
Distinct80
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size194.3 KiB
Minimum2022-07-13 00:00:00
Maximum2024-01-10 00:00:00
2024-05-17T22:47:15.309660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:15.496110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cleaning_fee
Real number (ℝ)

MISSING  ZEROS 

Distinct178
Distinct (%)1.1%
Missing9013
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean51.998564
Minimum0
Maximum566
Zeros4494
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:15.669187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39
Q378
95-th percentile169
Maximum566
Range566
Interquartile range (IQR)78

Descriptive statistics

Standard deviation58.669597
Coefficient of variation (CV)1.1282926
Kurtosis7.10233
Mean51.998564
Median Absolute Deviation (MAD)39
Skewness2.007014
Sum823449.26
Variance3442.1217
MonotonicityNot monotonic
2024-05-17T22:47:15.851490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4494
18.1%
39 574
 
2.3%
13 548
 
2.2%
65 512
 
2.1%
20 433
 
1.7%
33 432
 
1.7%
52 411
 
1.7%
26 363
 
1.5%
27 286
 
1.2%
46 285
 
1.1%
Other values (168) 7498
30.2%
(Missing) 9013
36.3%
ValueCountFrequency (%)
0 4494
18.1%
5 1
 
< 0.1%
6 11
 
< 0.1%
7 185
 
0.7%
8 23
 
0.1%
9 20
 
0.1%
10 65
 
0.3%
11 18
 
0.1%
12 67
 
0.3%
13 548
 
2.2%
ValueCountFrequency (%)
566 9
< 0.1%
408 12
< 0.1%
393 4
 
< 0.1%
391 12
< 0.1%
326 12
< 0.1%
320 12
< 0.1%
299 12
< 0.1%
290 6
< 0.1%
283 9
< 0.1%
274 11
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size194.3 KiB
Minimum2022-11-01 00:00:00
Maximum2023-10-01 00:00:00
2024-05-17T22:47:15.995444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:16.267268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

blocked_days
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3470965
Minimum0
Maximum30
Zeros5097
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:16.428805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile25
Maximum30
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1094892
Coefficient of variation (CV)1.2776691
Kurtosis1.037268
Mean6.3470965
Median Absolute Deviation (MAD)3
Skewness1.4791229
Sum157719
Variance65.763816
MonotonicityNot monotonic
2024-05-17T22:47:16.775379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 5097
20.5%
1 4895
19.7%
2 1984
 
8.0%
3 1959
 
7.9%
4 1312
 
5.3%
5 1068
 
4.3%
6 842
 
3.4%
8 685
 
2.8%
7 681
 
2.7%
25 521
 
2.1%
Other values (21) 5805
23.4%
ValueCountFrequency (%)
0 5097
20.5%
1 4895
19.7%
2 1984
 
8.0%
3 1959
 
7.9%
4 1312
 
5.3%
5 1068
 
4.3%
6 842
 
3.4%
7 681
 
2.7%
8 685
 
2.8%
9 497
 
2.0%
ValueCountFrequency (%)
30 171
 
0.7%
29 316
1.3%
28 169
 
0.7%
27 357
1.4%
26 192
 
0.8%
25 521
2.1%
24 217
0.9%
23 265
1.1%
22 227
0.9%
21 191
 
0.8%

available_days
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.652904
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:17.102800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q122
median28
Q330
95-th percentile31
Maximum31
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1094892
Coefficient of variation (CV)0.32894662
Kurtosis1.037268
Mean24.652904
Median Absolute Deviation (MAD)3
Skewness-1.4791229
Sum612600
Variance65.763816
MonotonicityNot monotonic
2024-05-17T22:47:17.464864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
31 5097
20.5%
30 4895
19.7%
29 1984
 
8.0%
28 1959
 
7.9%
27 1312
 
5.3%
26 1068
 
4.3%
25 842
 
3.4%
23 685
 
2.8%
24 681
 
2.7%
6 521
 
2.1%
Other values (21) 5805
23.4%
ValueCountFrequency (%)
1 171
 
0.7%
2 316
1.3%
3 169
 
0.7%
4 357
1.4%
5 192
 
0.8%
6 521
2.1%
7 217
0.9%
8 265
1.1%
9 227
0.9%
10 191
 
0.8%
ValueCountFrequency (%)
31 5097
20.5%
30 4895
19.7%
29 1984
 
8.0%
28 1959
 
7.9%
27 1312
 
5.3%
26 1068
 
4.3%
25 842
 
3.4%
24 681
 
2.7%
23 685
 
2.8%
22 497
 
2.0%

scraped_during_month
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
True
24849 
ValueCountFrequency (%)
True 24849
100.0%
2024-05-17T22:47:17.758802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

occupancy_rate
Real number (ℝ)

ZEROS 

Distinct259
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.041664
Minimum0
Maximum100
Zeros515
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:18.053809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q141.7
median70.3
Q396.9
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)55.2

Descriptive statistics

Standard deviation30.089373
Coefficient of variation (CV)0.45561198
Kurtosis-0.99308822
Mean66.041664
Median Absolute Deviation (MAD)27.7
Skewness-0.46199308
Sum1641069.3
Variance905.37037
MonotonicityNot monotonic
2024-05-17T22:47:18.451826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5823
 
23.4%
60 813
 
3.3%
0 515
 
2.1%
80 513
 
2.1%
40 457
 
1.8%
96 354
 
1.4%
72 336
 
1.4%
30 272
 
1.1%
48 267
 
1.1%
90 240
 
1.0%
Other values (249) 15259
61.4%
ValueCountFrequency (%)
0 515
2.1%
3.9 65
 
0.3%
4 36
 
0.1%
4.1 25
 
0.1%
4.3 20
 
0.1%
4.4 16
 
0.1%
4.6 6
 
< 0.1%
4.8 4
 
< 0.1%
5 9
 
< 0.1%
5.2 6
 
< 0.1%
ValueCountFrequency (%)
100 5823
23.4%
99.3 80
 
0.3%
99.1 31
 
0.1%
98.8 24
 
0.1%
98.6 72
 
0.3%
98.2 36
 
0.1%
97.8 79
 
0.3%
97.5 16
 
0.1%
97.1 29
 
0.1%
96.9 43
 
0.2%

reservation_days
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.021731
Minimum0
Maximum31
Zeros515
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:18.788447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q321
95-th percentile28
Maximum31
Range31
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3469042
Coefficient of variation (CV)0.59528342
Kurtosis-1.0367659
Mean14.021731
Median Absolute Deviation (MAD)7
Skewness0.13921398
Sum348426
Variance69.670809
MonotonicityNot monotonic
2024-05-17T22:47:19.142486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
4 1045
 
4.2%
13 950
 
3.8%
14 940
 
3.8%
17 937
 
3.8%
2 935
 
3.8%
15 925
 
3.7%
6 922
 
3.7%
11 920
 
3.7%
16 914
 
3.7%
8 910
 
3.7%
Other values (22) 15451
62.2%
ValueCountFrequency (%)
0 515
2.1%
1 754
3.0%
2 935
3.8%
3 830
3.3%
4 1045
4.2%
5 895
3.6%
6 922
3.7%
7 899
3.6%
8 910
3.7%
9 886
3.6%
ValueCountFrequency (%)
31 225
 
0.9%
30 424
1.7%
29 389
1.6%
28 452
1.8%
27 516
2.1%
26 610
2.5%
25 646
2.6%
24 717
2.9%
23 745
3.0%
22 835
3.4%

adr_usd
Real number (ℝ)

Distinct1068
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.49237
Minimum11
Maximum2708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:19.474367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile55
Q198
median147
Q3225
95-th percentile494
Maximum2708
Range2697
Interquartile range (IQR)127

Descriptive statistics

Standard deviation179.58253
Coefficient of variation (CV)0.91861656
Kurtosis25.289929
Mean195.49237
Median Absolute Deviation (MAD)58
Skewness4.0068602
Sum4857790
Variance32249.886
MonotonicityNot monotonic
2024-05-17T22:47:19.858130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 168
 
0.7%
112 167
 
0.7%
121 161
 
0.6%
124 155
 
0.6%
109 151
 
0.6%
123 151
 
0.6%
81 144
 
0.6%
110 144
 
0.6%
97 142
 
0.6%
95 142
 
0.6%
Other values (1058) 23324
93.9%
ValueCountFrequency (%)
11 2
 
< 0.1%
13 1
 
< 0.1%
14 4
 
< 0.1%
20 2
 
< 0.1%
21 3
 
< 0.1%
23 1
 
< 0.1%
24 10
< 0.1%
25 6
< 0.1%
26 5
< 0.1%
27 12
< 0.1%
ValueCountFrequency (%)
2708 1
< 0.1%
2603 1
< 0.1%
2420 1
< 0.1%
2339 1
< 0.1%
2278 1
< 0.1%
2239 1
< 0.1%
2238 1
< 0.1%
2159 1
< 0.1%
2141 1
< 0.1%
2046 1
< 0.1%

adr_native
Real number (ℝ)

Distinct890
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.43539
Minimum9
Maximum2139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:20.192642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile43
Q177
median116
Q3178
95-th percentile390
Maximum2139
Range2130
Interquartile range (IQR)101

Descriptive statistics

Standard deviation141.87083
Coefficient of variation (CV)0.91864197
Kurtosis25.291374
Mean154.43539
Median Absolute Deviation (MAD)46
Skewness4.0070545
Sum3837565
Variance20127.332
MonotonicityNot monotonic
2024-05-17T22:47:20.383684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 329
 
1.3%
88 294
 
1.2%
77 278
 
1.1%
100 270
 
1.1%
115 259
 
1.0%
73 257
 
1.0%
85 245
 
1.0%
111 245
 
1.0%
103 245
 
1.0%
92 244
 
1.0%
Other values (880) 22183
89.3%
ValueCountFrequency (%)
9 2
 
< 0.1%
10 1
 
< 0.1%
11 4
 
< 0.1%
16 2
 
< 0.1%
17 3
 
< 0.1%
18 1
 
< 0.1%
19 10
< 0.1%
20 6
 
< 0.1%
21 17
0.1%
22 4
 
< 0.1%
ValueCountFrequency (%)
2139 1
< 0.1%
2056 1
< 0.1%
1912 1
< 0.1%
1848 1
< 0.1%
1800 1
< 0.1%
1769 1
< 0.1%
1768 1
< 0.1%
1706 1
< 0.1%
1691 1
< 0.1%
1616 1
< 0.1%

number_of_reservation
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4161133
Minimum0
Maximum27
Zeros4837
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:20.680522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4963265
Coefficient of variation (CV)1.0234808
Kurtosis4.3073902
Mean3.4161133
Median Absolute Deviation (MAD)2
Skewness1.7557164
Sum84887
Variance12.224299
MonotonicityNot monotonic
2024-05-17T22:47:21.030356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 4837
19.5%
1 3915
15.8%
2 3661
14.7%
3 3012
12.1%
4 2520
10.1%
5 1763
 
7.1%
6 1328
 
5.3%
7 1000
 
4.0%
8 739
 
3.0%
9 535
 
2.2%
Other values (18) 1539
 
6.2%
ValueCountFrequency (%)
0 4837
19.5%
1 3915
15.8%
2 3661
14.7%
3 3012
12.1%
4 2520
10.1%
5 1763
 
7.1%
6 1328
 
5.3%
7 1000
 
4.0%
8 739
 
3.0%
9 535
 
2.2%
ValueCountFrequency (%)
27 2
 
< 0.1%
26 5
 
< 0.1%
25 5
 
< 0.1%
24 3
 
< 0.1%
23 8
 
< 0.1%
22 13
 
0.1%
21 22
0.1%
20 26
0.1%
19 27
0.1%
18 37
0.1%

revenue_usd
Real number (ℝ)

ZEROS 

Distinct6436
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2659.0292
Minimum0
Maximum64005
Zeros475
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:21.212581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile190
Q1969
median1980
Q33427
95-th percentile7293.6
Maximum64005
Range64005
Interquartile range (IQR)2458

Descriptive statistics

Standard deviation2809.9139
Coefficient of variation (CV)1.0567443
Kurtosis46.457334
Mean2659.0292
Median Absolute Deviation (MAD)1148
Skewness4.4818465
Sum66074216
Variance7895615.9
MonotonicityNot monotonic
2024-05-17T22:47:21.412030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
 
1.9%
1260 40
 
0.2%
1560 37
 
0.1%
1620 35
 
0.1%
1320 32
 
0.1%
1680 32
 
0.1%
480 32
 
0.1%
1728 32
 
0.1%
840 31
 
0.1%
1344 29
 
0.1%
Other values (6426) 24074
96.9%
ValueCountFrequency (%)
0 475
1.9%
11 1
 
< 0.1%
13 2
 
< 0.1%
14 2
 
< 0.1%
20 1
 
< 0.1%
25 2
 
< 0.1%
26 1
 
< 0.1%
28 3
 
< 0.1%
31 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
64005 1
< 0.1%
55886 1
< 0.1%
51532 1
< 0.1%
51424 1
< 0.1%
51314 1
< 0.1%
49654 1
< 0.1%
44425 1
< 0.1%
42426 1
< 0.1%
40894 1
< 0.1%
35355 1
< 0.1%

revenue_native
Real number (ℝ)

ZEROS 

Distinct5517
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2100.6317
Minimum0
Maximum50564
Zeros475
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-05-17T22:47:21.608369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150
Q1766
median1564
Q32707
95-th percentile5762
Maximum50564
Range50564
Interquartile range (IQR)1941

Descriptive statistics

Standard deviation2219.8291
Coefficient of variation (CV)1.0567436
Kurtosis46.457728
Mean2100.6317
Median Absolute Deviation (MAD)907
Skewness4.4818616
Sum52198596
Variance4927641.4
MonotonicityNot monotonic
2024-05-17T22:47:21.808370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
 
1.9%
995 42
 
0.2%
1232 38
 
0.2%
1280 35
 
0.1%
1706 35
 
0.1%
1217 34
 
0.1%
758 32
 
0.1%
1365 32
 
0.1%
379 32
 
0.1%
1770 32
 
0.1%
Other values (5507) 24062
96.8%
ValueCountFrequency (%)
0 475
1.9%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
16 1
 
< 0.1%
20 2
 
< 0.1%
21 1
 
< 0.1%
22 3
 
< 0.1%
24 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
50564 1
< 0.1%
44150 1
< 0.1%
40710 1
< 0.1%
40625 1
< 0.1%
40538 1
< 0.1%
39227 1
< 0.1%
35096 1
< 0.1%
33517 1
< 0.1%
32306 1
< 0.1%
27930 1
< 0.1%

Interactions

2024-05-17T22:47:06.105493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:21.816852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:25.338638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:27.655638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:30.383421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:33.707756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:37.041920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:39.903137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:43.017100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:46.167084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:49.975894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:53.431427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:57.213838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.092740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:02.697155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:06.340218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:22.051417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:25.475620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:27.815688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:30.609336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:33.892774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:37.230110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:40.139915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:43.154776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:46.446477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:50.139243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:53.658826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:57.439713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.226642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:02.924292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:06.487203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:22.288471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:25.626950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:27.968404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:30.848628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:34.134680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:37.427510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:40.330879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:43.387256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:46.754428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:50.342481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:53.923018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:57.738215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.362466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:03.163555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:06.636347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:22.552727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:25.792479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:28.130460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:31.092844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:34.377873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:37.659129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:40.487400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:43.646069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:47.055773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:50.606925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:54.345948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:57.981251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.507868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:03.556189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:06.783367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:22.788584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:25.931898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:28.282983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:31.293914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:34.616838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:37.886191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:40.646856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:43.897774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:47.316733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:50.870888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:54.511920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:58.177389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.645420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:03.743709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:06.918438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:22.990047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.075860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:28.430600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:31.491076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:34.849419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:38.110168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:40.849166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:44.042228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:47.527672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:51.099057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:54.811944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:58.410886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.856323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:03.979309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.055878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:23.219191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.220062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:28.576429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:31.728965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:35.082687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:38.274065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:41.074158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:44.191964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:47.751483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:51.360585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:55.110796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:58.617599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:00.983626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:04.203169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.198085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:23.451892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.370961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:28.729606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:31.969738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:35.306269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:38.425793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:41.305523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:44.561976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:47.990907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:51.500816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:55.368573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:58.826937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:01.123116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:04.440527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.341095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:23.703336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.525393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:28.896817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:32.210253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:35.528178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:38.569812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:41.548956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:44.709721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:48.245485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:51.739856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:55.598834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:58.978188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:01.265691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:04.679634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.478049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:23.991632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.669804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:29.037180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:32.431219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:35.677431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:38.722902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:41.785989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:44.843753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:48.550405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:51.984298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:55.851596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:59.209867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:01.401733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:04.906511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.616355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:24.259826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.821993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:29.217087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:32.625819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:36.026149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:38.898389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:42.018302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:44.986071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:48.859043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:52.209135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:56.084146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:59.340158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:01.623501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:05.062394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.780705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:24.496608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:26.961671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:29.460040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:32.825354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:36.261834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:39.135188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:42.253834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:45.189974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:49.111272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:52.456226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:56.323276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:59.469237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:01.827760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:05.284888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:07.972887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:24.646388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:27.102529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:29.697442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:33.037180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:36.455176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:39.352781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:42.450627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:45.411335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:49.373180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:52.682773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:56.537794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:59.599138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:02.015083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:05.514042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:08.106520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:24.890329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:27.246389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:29.897773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:33.267511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:36.604166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:39.595488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:42.594301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:45.645821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:49.584778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:52.927618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:56.800340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:59.828547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:02.249358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:05.731797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:08.251507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:25.100652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:27.398088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:30.139413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:33.504769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:36.845210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:39.751562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:42.772345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:45.863843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:49.734453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:53.178787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:56.985757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:46:59.966766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:02.464326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T22:47:05.896937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-17T22:47:08.495824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-17T22:47:09.010776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-17T22:47:09.451757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typelisting_typebedroomsbathroomscountry_codecityzipcodelatitudelongitudecurrency_nativeairbnb_property_idairbnb_host_idlast_seencleaning_feereporting_monthblocked_daysavailable_daysscraped_during_monthoccupancy_ratereservation_daysadr_usdadr_nativenumber_of_reservationrevenue_usdrevenue_native
0Entire homeentire_home22GBBrightonBN1 1BG50.82214-0.14175USD748193946342024-01-1046.02023-10031True46.512244193129742349
1Entire rental unitentire_home11GBBrightonBN1 4AQ50.82745-0.14099USD23078712062082024-01-10NaN2023-10031True7.721711352342270
2Private room in rental unitprivate_room11GBBrightonBN3 4GP50.82537-0.18462USD28098214648112024-01-1010.02023-10031True15.5473582312246
3Entire townhouseentire_home63GBBrightonBN2 9XH50.82828-0.12467USD2820043571612024-01-10260.02023-10031True0.00537424000
4Entire rental unitentire_home12GBHoveBN3 3UA50.83128-0.16963USD33846617185062024-01-1033.02023-10031True100.0319575029452327
5Entire rental unitentire_home11GBHoveBN3 3AB50.82738-0.16405USD33905417185062024-01-1013.02023-10031True100.0318870027282155
6Entire rental unitentire_home11GBBrightonBN2 1RJ50.82123-0.13470USD3809241270092024-01-10NaN2023-10031True65.81711288419041504
7Entire rental unitentire_home11GBHoveBN3 1HE50.82496-0.16139USD47862522981362024-01-100.02023-10031True100.027138109037262944
8Entire rental unitentire_home32GBBrightonBN2 3DD50.83200-0.12900USD54377026733332024-01-10156.02023-10031True15.54419331118321447
9Entire homeentire_home32GBBrightonBN2 9PE50.82582-0.12841USD56886127995952024-01-1065.02023-10724True70.014244193134812750
property_typelisting_typebedroomsbathroomscountry_codecityzipcodelatitudelongitudecurrency_nativeairbnb_property_idairbnb_host_idlast_seencleaning_feereporting_monthblocked_daysavailable_daysscraped_during_monthoccupancy_ratereservation_daysadr_usdadr_nativenumber_of_reservationrevenue_usdrevenue_native
24839Entire rental unitentire_home32GBBrighton and HoveBN1 3AU50.83004-0.15090USD542569212797140132024-01-03NaN2022-111516True60.0815111911208954
24840Private room in bed and breakfastprivate_room11GBKemptownBN2 0GA50.82272-0.12911USD542905032409611522023-11-29NaN2022-11130True68.017175138429752350
24841Entire rental unitentire_home21GBBrighton and HoveBN2 3LQ50.83581-0.12174USD54302085827023762022-11-16NaN2022-11229True78.61912296623181831
24842Entire rental unitentire_home31GBHoveBN3 1AE50.82582-0.15627USD543363131677626202024-01-03NaN2022-11130True28.0714411421008796
24843Private room in guesthouseprivate_room11GBKemptownBN2 1ET50.81930-0.13528USD543405643236242632024-01-03NaN2022-11427True13.33113891339268
24844Entire vacation homeentire_home21GBKemptownBN2 1PB50.82000-0.13200USD543473931767899492024-01-03NaN2022-11130True100.026151119539263102
24845Private room in townhouseprivate_roomStudio0GBBrighton and HoveBN2 9NY50.82823-0.12913USD54351505416203262024-01-03NaN2022-11229True37.2981644729576
24846Entire condoentire_home12GBBrighton and HoveBN3 3QB50.83145-0.16843USD543685791680751112023-05-17NaN2022-11256True20.0110482010482
24847Entire condoentire_home11GBBrighton and HoveBN3 2DL50.82802-0.17510USD5438648232892232024-01-03NaN2022-11526True55.41282655984777
24848Entire condoentire_home11GBBrighton and HoveBN1 8WA50.85864-0.15201USD544092284367675642024-01-03NaN2022-111714True8.61665206652